Application of Bayesian approaches in drug development: starting a virtuous cycle

Bayesian Analysis of Modified Nutrition Risk in Critically Ill (mNUTRIC) Score for Mortality Prediction in Critically Ill Patients

Background and aims

Malnutrition has a considerable influence on critically ill patients by increasing mortality and poorer clinical outcomes. The modified Nutrition Risk in Critically Ill (mNUTRIC) score is commonly used to assess nutritional risk and predict death; however, its sensitivity, specificity, and optimal cut-off values differ between studies. This study uses a Bayesian approach to assess the accuracy of the mNUTRIC score in predicting mortality in critically ill patients.

Patients and methods

A preplanned Bayesian analysis was performed using data from 31 cohort studies, which included 13,271 intensive care unit (ICU) patients. The study investigated the mNUTRIC score's sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC). Subgroup analysis compared mortality rates at 28-day, 90-day, and in-hospital time points, along with cut-off values (<5 vs ≥5). Bayesian modeling was performed using the rjags and brms packages in R version 3.2.1. These tools also facilitated the visualization of results, including posterior distributions, forest plots, and Fagan nomograms.

Results

Bayesian analysis affirmed the mNUTRIC score's high discriminative capacity, with a pooled sensitivity of 0.84 (95% credible interval (CrI): 0.80-0.88), specificity of 0.77 (95% CrI: 0.73-0.80), and AUC of 0.88 (95% CrI: 0.83-0.92). A cut-off of <5 resulted in higher sensitivity (0.83) and AUC (0.87), whereas ≥5 remained accurate but had somewhat lower sensitivity. The score consistently predicted 28-day, 90-day, and in-hospital mortality.

Conclusions

The Bayesian analysis validates the mNUTRIC score as a reliable predictor of mortality in critically ill patients. Its excellent diagnostic performance suggests its incorporation into ICU for early risk assessment and nutritional interventions.

How to cite this article

Prakash J, Saran K, Verma V, Raj K, Kumari A, Bhattacharya PK, et al. Bayesian Analysis of Modified Nutrition Risk in Critically Ill (mNUTRIC) Score for Mortality Prediction in Critically Ill Patients. Indian J Crit Care Med 2025;29(5):449-457.

Use of Bayesian approaches in oncology clinical trials: A cross-sectional analysis

Purpose

Bayesian approaches may improve the efficiency of trials and accelerate decision-making, but reluctance to depart from traditional frequentist statistics may limit their use. Because oncology trials generally involve severe conditions with no or limited therapeutic options, they are well-suited to applying Bayesian methodologies and are perceived as using these methods often in early phases.

Objectives

In this study, we aim to describe the use of Bayesian methods and designs in oncology clinical trials in the last 20 years.

Method

A cross-sectional observational study was conducted to identify oncology clinical trials using Bayesian approaches registered in clinicaltrials.gov between 2004 and 2024. Trials were searched in clinicaltrials.gov, PubMed, and through manual search of cross-references.

Results

Bayesian trials were retrieved, and their main characteristics were extracted using R and verified manually. Between 2004 and 2024, 384,298 trials were registered in clinicaltrials.gov; we identified 84,850 oncology clinical trials (22%), of which 640 (0.75%) used Bayesian approaches. The adoption of Bayesian trials increased significantly after 2011, but while half of all Bayesian studies started in the last 5 years, this paralleled the overall increase in oncology research rather than an increase in the proportion of Bayesian trials. The majority of Bayesian trials were phase 1 and phase 2 studies, and two-thirds of Bayesian trials with efficacy objectives had single-arm designs, often utilizing binary endpoints, such as overall response, as the primary measure.

Conclusion

The uptake of Bayesian methods in oncology clinical trials has flattened and is still scarce, and is mostly applied to the analysis of treatment efficacy in single-arm trials with binary endpoints. There is room for further uptake and use of their potential advantages in settings with small populations and severe conditions with unmet needs.

A Bayesian Interpretation of CABANA and Other Randomized Controlled Trials for Catheter Ablation in Patients With Atrial Fibrillation

BACKGROUND

Catheter ablation improves symptoms and quality of life in atrial fibrillation patients, but its effect on adverse cardiovascular outcomes and mortality remains uncertain. Bayesian analysis of randomized controlled trials offers a deeper understanding of treatment effects beyond conventional p-value thresholds.

METHODS

We conducted a post hoc Bayesian reanalysis of CABANA and four similar trials to estimate catheter ablation's effect on cardiovascular and survival outcomes. Using publicly available, trial-level data, we fitted ordinal Bayesian regression models to assess the impact of catheter ablation on the primary composite outcome-comprising all-cause mortality, stroke with disability, serious bleeding, and cardiac arrest-as well as mortality alone. We considered two sets of prior distributions: (1) a noninformative prior, where all effect sizes are equally probable and inference is primarily based on trial data, and (2) a treatment effect distribution derived from four trials using a random effects model.

RESULTS

In this analysis, refined probability distributions for treatment effects were obtained by integrating data from CABANA with diverse priors through Bayes' theorem, offering a novel, nuanced probabilistic understanding of the potential impact of ablation compared with medical therapy on cardiovascular outcomes and all-cause mortality. In contrast to CABANA's original frequentist estimates, which were inconclusive, Bayesian analyses indicated probabilities of 82.6% and 81.1% that ablation is superior in reducing adverse cardiovascular outcomes and mortality, respectively. Incorporating results from four other similar trials increased the probability of improved effects on mortality to 86.0%.

CONCLUSIONS

Bayesian analysis augmented the interpretation of previously inconclusive findings, suggesting a clinically relevant probability of benefit from catheter ablation compared to medical therapy in a broad population with atrial fibrillation.

An evolutionary tale on clinical trials in hidradenitis suppurativa

The therapeutic pipeline for moderate-to-severe hidradenitis suppurativa (HS) is robust. Successes and lessons learned have led to improvements in trial designs aimed at avoiding prior pitfalls, as well as high placebo response in HS, which remains a fundamental threat to drug development. Herein, we review the evolutions in HS trials over the last 20 years with respect to overall design, sample size, diversity in enrolment, inclusion criteria, concomitant medications, rescue therapy, endpoints and statistical design analysis plans. Areas of focus that merit future consideration are also highlighted.

Adaptive Universal Principles for Real-world Observational Studies (AUPROS): an approach to designing real-world observational studies for clinical, epidemiologic, and precision oncology research

The field of precision oncology has witnessed several advances that stimulated the development of new clinical trial designs and the emergence of real-world data (RWD) as an important resource for evidence generation in healthcare decision-making. Here, we highlight our experience with an innovative approach to a set of Adaptive, Universal Principles for Real-world Observational Studies (AUPROS). To demonstrate the utility of these principles, we used a mixed-methods approach to assess three studies that follow AUPROS at Princess Margaret Cancer Centre: (1) Molecular Epidemiology of ThorAcic Lesions (METAL), (2) Translational Head And NecK Study (THANKS), and (3) CAnadian CAncers With Rare Molecular Alterations (CARMA; NCT04151342). We performed resource assessments, stakeholder-directed surveys and discussions, analysis of funding, research output, collaborations, and a Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Based on these analyses, AUPROS is an approach that is applicable to a wide range of observational study designs. The universality of AUPROS allows for multi-purpose analyses of various RWD, and the adaptive nature creates opportunities for multi-source funding and collaborations. Following AUPROS can offer cost and logistical benefits and may lead to increased research productivity. Several challenges were identified pertinent to ethics approvals, sustainability, complex coordination, and data quality that require local adaptation of these principles.

The Future of Clinical Trials in Inflammatory Bowel Disease

The medical management of inflammatory bowel disease (IBD) has been transformed over the past few decades by the approval of multiple classes of advanced therapies and the integration of more targeted treatment strategies for Crohn's disease and ulcerative colitis. These changes have been driven by an increasing number of pivotal randomized controlled trials, which have grown in size and complexity over time. Several landmark studies that are anticipated to change current IBD management paradigms have recently been completed or are on-going, including the first head-to-head biologic trials, advanced combination treatment trials, therapeutic strategy and treatment target trials, and multiple phase 3 registrational programs of novel compounds. Despite these advances, the future of IBD trials also faces major challenges with respect to cost, feasibility, and recruitment. Accordingly, innovative methods for early and late phase randomized controlled trials must be adopted. In this review, we provide a comprehensive overview of the evolution of modern IBD trials, discuss methods for improving trial efficiency in early and late phase development, and provide insights into the interpretation and implications of these data for clinical care.

Leptomeningeal metastatic disease: new frontiers and future directions

Leptomeningeal metastatic disease (LMD), encompassing entities of 'meningeal carcinomatosis', neoplastic meningitis' and 'leukaemic/lymphomatous meningitis', arises secondary to the metastatic dissemination of cancer cells from extracranial and certain intracranial malignancies into the leptomeninges and cerebrospinal fluid. The clinical burden of LMD has been increasing secondary to more sensitive diagnostics, aggressive local therapies for discrete brain metastases, and improved management of extracranial disease with targeted and immunotherapeutic agents, resulting in improved survival. However, owing to drug delivery challenges and the unique microenvironment of LMD, novel therapies against systemic disease have not yet translated into improved outcomes for these patients. Underdiagnosis and misdiagnosis are common, response assessment remains challenging, and the prognosis associated with this disease of whole neuroaxis remains extremely poor. The dearth of effective therapies is further challenged by the difficulties in studying this dynamic disease state. In this Review, a multidisciplinary group of experts describe the emerging evidence and areas of active investigation in LMD and provide directed recommendations for future research. Drawing upon paradigm-changing advances in mechanistic science, computational approaches, and trial design, the authors discuss domain-specific and cross-disciplinary strategies for optimizing the clinical and translational research landscape for LMD. Advances in diagnostics, multi-agent intrathecal therapies, cell-based therapies, immunotherapies, proton craniospinal irradiation and ongoing clinical trials offer hope for improving outcomes for patients with LMD.

Bayesian Predictive Probability Based on a Bivariate Index Vector for Single-Arm Phase II Study With Binary Efficacy and Safety Endpoints

In oncology, Phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent Phase III trials. Traditionally, Phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important consideration. In the context of such multiple-outcome designs, predictive probability-based Bayesian monitoring strategies have been developed to assess whether a clinical trial will provide enough evidence to continue with a Phase III study at the scheduled end of the trial. Therefore, we propose a new simple index vector to summarize the results that cannot be captured by existing strategies. Specifically, we define the worst and most promising situations for the potential effect of a treatment, then use the proposed index vector to measure the deviation between the two situations. Finally, simulation studies are performed to evaluate the operating characteristics of the design. The obtained results demonstrate that the proposed method makes appropriate interim go/no-go decisions.

Delta Inflation, Optimism Bias, and Uncertainty in Clinical Trials

The phenomenon of delta inflation, in which design treatment effects tend to exceed observed treatment effects, has been documented in several therapeutic areas. Delta inflation has often been attributed to investigators' optimism bias, or an unwarranted belief in the efficacy of new treatments. In contrast, we argue that delta inflation may be a natural consequence of clinical equipoise, that is, genuine uncertainty about the relative benefits of treatments before a trial is initiated. We review alternative methodologies that can offer more direct evidence about investigators' beliefs, including Bayesian priors and forecasting analysis. The available evidence for optimism bias appears to be mixed, and can be assessed only where uncertainty is expressed explicitly at the trial design stage.

An Introduction to Bayesian Approaches to Trial Design and Statistics for Stroke Researchers

While the majority of stroke researchers use frequentist statistics to analyze and present their data, Bayesian statistics are becoming more and more prevalent in stroke research. As opposed to frequentist approaches, which are based on the probability that data equal specific values given underlying unknown parameters, Bayesian approaches are based on the probability that parameters equal specific values given observed data and prior beliefs. The Bayesian paradigm allows researchers to update their beliefs with observed data to provide probabilistic interpretations of key parameters, for example, the probability that a treatment is effective. In this review, we outline the basic concepts of Bayesian statistics as they apply to stroke trials, compare them to the frequentist approach using exemplary data from a randomized trial, and explain how a Bayesian analysis is conducted and interpreted.

Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature

BACKGROUND

In randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships.

METHODS

We conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings.

RESULTS

We identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used.

CONCLUSIONS

We found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.

Drug-Drug Interactions and Synergy: From Pharmacological Models to Clinical Application

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. SIGNIFICANCE STATEMENT: Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug-drug interactions research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Adjunctive Pascolizumab in Rifampicin-Susceptible Pulmonary Tuberculosis: Proof-of-Concept, Partially-Randomized, Double-Blind, Placebo-Controlled, Dose-Escalation Trial

BACKGROUND

Interleukin 4 (IL-4), increased in tuberculosis infection, may impair bacterial killing. Blocking IL-4 confers benefit in animal models. We evaluated safety and efficacy of pascolizumab (humanized anti-IL-4 monoclonal antibody) as adjunctive tuberculosis treatment.

METHODS

Participants with rifampicin-susceptible pulmonary tuberculosis received a single intravenous infusion of pascolizumab or placebo, and standard 6-month tuberculosis treatment. Pascolizumab dose increased in successive cohorts: (1) nonrandomized 0.05 mg/kg (n = 4); (2) nonrandomized 0.5 mg/kg (n = 4); (3) randomized 2.5 mg/kg (n = 9) or placebo (n = 3); and (4) randomized 10 mg/kg (n = 9) or placebo (n = 3). Coprimary safety outcome was study-drug-related grade 4 or serious adverse event (G4/SAE) in all cohorts (1-4). Coprimary efficacy outcome was week 8 sputum culture time-to-positivity (TTP) in randomized cohorts (3-4) combined.

RESULTS

Pascolizumab levels exceeded IL-4 50% neutralizing dose for 8 weeks in 78%-100% of participants in cohorts 3-4. There were no study-drug-related G4/SAEs. Median week-8 TTP was 42 days in pascolizumab and placebo groups (P = .185). Rate of TTP increase was greater with pascolizumab (difference from placebo 0.011 log10 TTP/day; 95% Bayesian credible interval 0.006 to 0.015 log10 TTP/day).

CONCLUSIONS

There was no evidence to suggest blocking IL-4 was unsafe. Preliminary efficacy findings are consistent with animal models. This supports further investigation of adjunctive anti-IL-4 interventions for tuberculosis in larger phase 2 trials.

CLINICAL TRIALS REGISTRATION

NCT01638520.

Maintenance of zilucoplan efficacy in patients with generalised myasthenia gravis up to 24 weeks: a model-informed analysis

Background

Clinical efficacy of zilucoplan has been demonstrated in a 12-week, placebo-controlled, phase III study in patients with acetylcholine receptor autoantibody-positive generalised myasthenia gravis (gMG). However, placebo-controlled zilucoplan data past 12 weeks are not available.

Objectives

Predict the treatment effect of zilucoplan versus control (placebo or standard of care) in patients with gMG up to 24 weeks.

Design

A model-informed analysis (MIA) within a Bayesian framework.

Methods

Part 1 of the MIA comprised a control meta-regression using aggregate data on control response over time from randomised studies and a national myasthenia gravis (MG) registry. In Part 2, a combined Bayesian analysis of individual patient-level data from the phase II (NCT03315130), RAISE (NCT04115293) and RAISE-XT (NCT04225871) studies of zilucoplan was conducted using posterior distributions from Part 1 as informative priors. Population mean treatment effect in the change from baseline (CFB) at week 24 in MG-Activities of Daily Living (MG-ADL) and quantitative MG (QMG) scores for zilucoplan versus control were assessed.

Results

At week 24, the predicted mean CFB in MG-ADL score was -4.55 (95% credible interval: -6.04, -3.13) with zilucoplan versus -2.00 (-3.35, -0.64) with control (difference: -2.55 [-3.76, -1.40]). The probability of a favourable treatment effect as measured by MG-ADL score at week 24 with zilucoplan versus control was >99.9%. There was an 82.8% probability that the difference in the predicted mean CFB in MG-ADL score at week 24 was greater than the clinically meaningful threshold (⩾2.0-point improvement). Comparable results were observed with QMG.

Conclusion

This MIA demonstrates the maintenance of efficacy with zilucoplan versus control up to 24 weeks. Through combining real-world evidence with data from randomised studies, this novel method to estimate long-term treatment efficacy facilitated reduced exposure to placebo in the phase III RAISE study. This methodology could be used to reduce the length of future placebo-controlled studies.

Multidisciplinary considerations for implementing Bayesian borrowing in basket trials

Drug development has historically relied on phase I-III clinical trials including participants sharing the same disease. However, drug development has evolved as the discovery of mechanistic drivers of disease demonstrated that the same therapeutic target may provide benefits across different diseases. A basket trial condenses evaluation of one therapy among multiple related diseases into a single trial and presents an opportunity to borrow information across them rather than viewing each in isolation. Borrowing is a statistical tool but requires a foundation of clinical and therapeutic mechanistic justification. We review the Bayesian borrowing approach, including its assumptions, and provide a framework for how this approach can be evaluated for successful use in a basket trial for drug development.

Effectiveness of dexmedetomidine on patient-centred outcomes in surgical patients: a systematic review and Bayesian meta-analysis

BACKGROUND

Dexmedetomidine is increasingly used for surgical patients requiring general anaesthesia. However, its effectiveness on patient-centred outcomes remains uncertain. Our main objective was to evaluate the patient-centred effectiveness of intraoperative dexmedetomidine for adult patients requiring surgery under general anaesthesia.

METHODS

We conducted a systematic search of MEDLINE, Embase, CENTRAL, Web of Science, and CINAHL from inception to October 2023. Randomised controlled trials (RCTs) comparing intraoperative use of dexmedetomidine with placebo, opioid, or usual care in adult patients requiring surgery under general anaesthesia were included. Study selection, data extraction, and risk of bias assessment were performed by two reviewers independently. We synthesised data using a random-effects Bayesian regression framework to derive effect estimates and the probability of a clinically important effect. For continuous outcomes, we pooled instruments with similar constructs using standardised mean differences (SMDs) and converted SMDs and credible intervals (CrIs) to their original scale when appropriate. We assessed the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Our primary outcome was quality of recovery after surgery. To guide interpretation on the original scale, the Quality of Recovery-15 (QoR-15) instrument was used (range 0-150 points, minimally important difference [MID] of 6 points).

RESULTS

We identified 49,069 citations, from which 44 RCTs involving 5904 participants were eligible. Intraoperative dexmedetomidine administration was associated with improvement in postoperative QoR-15 (mean difference 9, 95% CrI 4-14, n=21 RCTs, moderate certainty of evidence). We found 99% probability of any benefit and 88% probability of achieving the MID. There was a reduction in chronic pain incidence (odds ratio [OR] 0.42, 95% CrI 0.19-0.79, n=7 RCTs, low certainty of evidence). There was also increased risk of clinically significant hypotension (OR 1.98, 95% CrI 0.84-3.92, posterior probability of harm 94%, n=8 RCTs) and clinically significant bradycardia (OR 1.74, 95% CrI 0.93-3.34, posterior probability of harm 95%, n=10 RCTs), with very low certainty of evidence for both. There was limited evidence to inform other secondary patient-centred outcomes.

CONCLUSIONS

Compared with placebo or standard of care, intraoperative dexmedetomidine likely results in meaningful improvement in the quality of recovery and chronic pain after surgery. However, it might increase clinically important bradycardia and hypotension.

SYSTEMATIC REVIEW PROTOCOL

PROSPERO (CRD42023439896).

Toward Patient Centricity: Why Do Patients With Inflammatory Bowel Disease Participate in Pharmaceutical Clinical Trials? A Mixed-Methods Exploration of Study Participants

Background

A better understanding of motivations to participate as well as recommendations to reduce barriers to enrollment may assist in design of future clinical trials.

Methods

We developed a 32-item electronic questionnaire to explore motivations, experiences, and recommendations of inflammatory bowel disease patients, who had participated in pharmaceutical clinical trials in a tertiary center in Canada over the last decade. We employed a mixed-methods approach that integrates both quantitative and qualitative research methods.

Results

We distributed a total of 69 e-mails with surveys and received 46 responses (66.6% response rate). Study participants were mostly male (27/46, 58.7%), non-Hispanic White (43/46, 93.5%), with a mean age of 45.5 years (SD 10.9). Most decided to participate in a clinical trial to benefit future patients (29/46, 63.0%). Half of the participants (23/46, 50.0%) reported they were worried about the possibility of receiving placebo, although the majority (29/46, 63.0%) understood they could improve on placebo. The most challenging aspect reported was the number and length of questionnaires (15/46, 32.6%), as well as the number of colonoscopies (14/46, 30.4%). Strategies recommended to increase enrollment were reduction of the chance of receiving placebo (20/46, 43.5%), facilitating inclusion of patients who have failed multiple therapies (20/46, 43.5%), allowing virtual visits (18/46, 39.1%), including subtypes of disease traditionally excluded from trials (16/46, 34.8%) and improving outreach to underrepresented populations (13/46, 28.3%). The vast majority (37/46, 80.4%) reported their experience of participation to be better than expected.

Conclusions

These results should help inform the design of future clinical trials with a focus on patient-centricity.

Benford's Law and distributions for better drug design

INTRODUCTION

Modern drug discovery incorporates various tools and data, heralding the beginning of the data-driven drug design (DD) era. The distributions of chemical and physical data used for Artificial Intelligence (AI)/Machine Learning (ML) and to drive DD have thus become highly important to be understood and used effectively.

AREAS COVERED

The authors perform a comprehensive exploration of the statistical distributions driving the data-intensive era of drug discovery, including Benford's Law in AI/ML-based DD.

EXPERT OPINION

As the relevance of data-driven discovery escalates, we anticipate meticulous scrutiny of datasets utilizing principles like Benford's Law to enhance data integrity and guide efficient resource allocation and experimental planning. In this data-driven era of the pharmaceutical and medical industries, addressing critical aspects such as bias mitigation, algorithm effectiveness, data stewardship, effects, and fraud prevention are essential. Harnessing Benford's Law and other distributions and statistical tests in DD provides a potent strategy to detect data anomalies, fill data gaps, and enhance dataset quality. Benford's Law is a fast method for data integrity and quality of datasets, the backbone of AI/ML and other modeling approaches, proving very useful in the design process.

Estimating the effect of nintedanib on forced vital capacity in children and adolescents with fibrosing interstitial lung disease using a Bayesian dynamic borrowing approach

BACKGROUND

The rarity of childhood interstitial lung disease (chILD) makes it challenging to conduct powered trials. In the InPedILD trial, among 39 children and adolescents with fibrosing ILD, there was a numerical benefit of nintedanib versus placebo on change in forced vital capacity (FVC) over 24 weeks (difference in mean change in FVC % predicted of 1.21 [95% confidence interval: -3.40, 5.81]). Nintedanib has shown a consistent effect on FVC across populations of adults with different diagnoses of fibrosing ILD.

METHODS

In a Bayesian dynamic borrowing analysis, prespecified before data unblinding, we incorporated data on the effect of nintedanib in adults and the data from the InPedILD trial to estimate the effect of nintedanib on FVC in children and adolescents with fibrosing ILD. The data from adults were represented as a meta-analytic predictive (MAP) prior distribution with mean 1.69 (95% credible interval: 0.49, 3.08). The adult data were weighted according to expert judgment on their relevance to the efficacy of nintedanib in chILD, obtained in a formal elicitation exercise.

RESULTS

Combined data from the MAP prior and InPedILD trial analyzed within the Bayesian framework resulted in a median difference between nintedanib and placebo in change in FVC % predicted at Week 24 of 1.63 (95% credible interval: -0.69, 3.40). The posterior probability for superiority of nintedanib versus placebo was 95.5%, reaching the predefined success criterion of at least 90%.

CONCLUSION

These findings, together with the safety data from the InPedILD trial, support the use of nintedanib in children and adolescents with fibrosing ILDs.

Comparison of Quality of Recovery between Modified Thoracoabdominal Nerves Block through Perichondrial Approach versus Oblique Subcostal Transversus Abdominis Plane Block in Patients Undergoing Total Laparoscopic Hysterectomy: A Pilot Randomized Controlled Trial

Modified thoracoabdominal nerves block through a perichondrial approach (M-TAPA) provides a wide analgesic range. Herein, we examined the quality of recovery (QoR) of M-TAPA for total laparoscopic hysterectomy (TLH) compared with oblique subcostal transversus abdominis plane block (OSTAPB) and measured plasma levobupivacaine concentrations (PClevo). Forty female patients undergoing TLH were randomized to each group. Nerve blocks were performed bilaterally with 25 mL of 0.25% levobupivacaine administered per side. The primary outcome was changes in QoR-15 scores on postoperative days (POD) 1 and 2 from the preoperative baseline. The main secondary outcomes were PClevo at 15, 30, 45, 60, and 120 min after performing nerve block. Group differences (M-TAPA-OSTAPB) in mean changes from baseline in QoR-15 scores on POD 1 and 2 were -11.3 (95% confidence interval (CI), -24.9 to 2.4, p = 0.104; standard deviation (SD), 22.8) and -7.0 (95% CI, -20.5 to 6.6, p = 0.307; SD, 18.7), respectively. Changes in PClevo were similar in both groups. The post hoc analysis using Bayesian statistics revealed that posterior probabilities of M-TAPA being clinically more effective than OSTAPB were up to 22.4 and 24.4% for POD 1 and 2, respectively. In conclusion, M-TAPA may not be superior to OSTAPB for TLH.

Inflammation across tissues: can shared cell biology help design smarter trials?

Immune-mediated inflammatory diseases (IMIDs) are responsible for substantial global disease burden and associated health-care costs. Traditional models of research and service delivery silo their management within organ-based medical disciplines. Very often patients with disease in one organ have comorbid involvement in another, suggesting shared pathogenic pathways. Moreover, different IMIDs are often treated with the same drugs (including glucocorticoids, immunoregulators and biologics). Unlocking the cellular basis of these diseases remains a major challenge, leading us to ask why, if these diseases have so much in common, they are not investigated in a common manner. A tissue-based, cellular understanding of inflammation might pave the way for cross-disease, cross-discipline basket trials (testing one drug across two or more diseases) to reduce the risk of failure of early-phase drug development in IMIDs. This new approach will enable rapid assessment of the efficacy of new therapeutic agents in cross-disease translational research in humans.

Interpreting Randomized Controlled Trials

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

Which severe COVID-19 patients could benefit from high dose dexamethasone? A Bayesian post-hoc reanalysis of the COVIDICUS randomized clinical trial

BACKGROUND

The respective benefits of high and low doses of dexamethasone (DXM) in patients with severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) and acute respiratory failure (ARF) are controversial, with two large triple-blind RCTs reaching very important difference in the effect-size. In the COVIDICUS trial, no evidence of additional benefit of high-dose dexamethasone (DXM20) was found. We aimed to explore whether some specific patient phenotypes could benefit from DXM20 compared to the standard of care 6 mg dose of DXM (DXMSoC).

METHODS

We performed a post hoc exploratory Bayesian analysis of 473 patients who received either DXMSoc or DXM20 in the COVIDICUS trial. The outcome was the 60 day mortality rate of DXM20 over DXMSoC, with treatment effect measured on the hazard ratio (HR) estimated from Cox model. Bayesian analyses allowed to compute the posterior probability of a more than trivial benefit (HR < 0.95), and that of a potential harm (HR > 1.05). Bayesian measures of interaction then quantified the probability of interaction (Pr Interact) that the HR of death differed across the subsets by 20%. Primary analyses used noninformative priors, centred on HR = 1.00. Sensitivity analyses used sceptical and enthusiastic priors, based on null (HR = 1.00) or benefit (HR = 0.95) effects.

RESULTS

Overall, the posterior probability of a more than trivial benefit and potential harm was 29.0 and 51.1%, respectively. There was some evidence of treatment by subset interaction (i) according to age (Pr Interact, 84%), with a 86.5% probability of benefit in patients aged below 70 compared to 22% in those aged above 70; (ii) according to the time since symptoms onset (Pr Interact, 99%), with a 99.9% probability of a more than trivial benefit when lower than 7 days compared to a < 0.1% probability when delayed by 7 days or more; and (iii) according to use of remdesivir (Pr Interact, 91%), with a 90.1% probability of benefit in patients receiving remdesivir compared to 19.1% in those who did not.

CONCLUSIONS

In this exploratory post hoc Bayesian analysis, compared with standard-of-care DXM, high-dose DXM may benefit patients aged less than 70 years with severe ARF that occurred less than 7 days after symptoms onset. The use of remdesivir may also favour the benefit of DXM20. Further analysis is needed to confirm these findings.

TRIAL REGISTRATION

NCT04344730, date of registration April 14, 2020 ( https://clinicaltrials.gov/ct2/show/NCT04344730?term=NCT04344730&draw=2&rank=1 ); EudraCT: 2020-001457-43 ( https://www.clinicaltrialsregister.eu/ctr-search/search?query=2020-001457-43 ).

Comparison of Cystatin C and Creatinine in the Assessment of Measured Kidney Function during Critical Illness

BACKGROUND

Incomplete recovery of kidney function is an important adverse outcome in survivors of critical illness. However, unlike eGFR creatinine, eGFR cystatin C is not confounded by muscle loss and may improve identification of persistent kidney dysfunction.

METHODS

To assess kidney function during prolonged critical illness, we enrolled 38 mechanically ventilated patients with an expected length of stay of >72 hours near admission to intensive care unit (ICU) in a single academic medical center. We assessed sequential kidney function using creatinine, cystatin C, and iohexol clearance measurements. The primary outcome was difference between eGFR creatinine and eGFR cystatin C at ICU discharge using Bayesian regression modeling. We simultaneously measured muscle mass by ultrasound of the rectus femoris to assess the confounding effect on serum creatinine generation.

RESULTS

Longer length of ICU stay was associated with greater difference between eGFR creatinine and eGFR cystatin C at a predicted rate of 2 ml/min per 1.73 m 2 per day (95% confidence interval [CI], 1 to 2). By ICU discharge, the posterior mean difference between creatinine and cystatin C eGFR was 33 ml/min per 1.73 m 2 (95% credible interval [CrI], 24 to 42). In 27 patients with iohexol clearance measured close to ICU discharge, eGFR creatinine was on average two-fold greater than the iohexol gold standard, and posterior mean difference was 59 ml/min per 1.73 m 2 (95% CrI, 49 to 69). The posterior mean for eGFR cystatin C suggested a 22 ml/min per 1.73 m 2 (95% CrI, 13 to 31) overestimation of measured GFR. Each day in ICU resulted in a predicted 2% (95% CI, 1% to 3%) decrease in muscle area. Change in creatinine-to-cystatin C ratio showed good longitudinal, repeated measures correlation with muscle loss, R =0.61 (95% CI, 0.50 to 0.72).

CONCLUSIONS

eGFR creatinine systematically overestimated kidney function after prolonged critical illness. Cystatin C better estimated true kidney function because it seemed unaffected by the muscle loss from prolonged critical illness.

CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER

Skeletal Muscle Wasting and Renal Dysfunction After Critical Illness Trauma - Outcomes Study (KRATOS), NCT03736005 .

A SuperLearner-enforced approach for the estimation of treatment effect in pediatric trials

Background

Randomized Clinical Trials (RCT) represent the gold standard among scientific evidence. RCTs are tailored to control selection bias and the confounding effect of baseline characteristics on the effect of treatment. However, trial conduction and enrolment procedures could be challenging, especially for rare diseases and paediatric research. In these research frameworks, the treatment effect estimation could be compromised. A potential countermeasure is to develop predictive models on the probability of the baseline disease based on previously collected observational data. Machine learning (ML) algorithms have recently become attractive in clinical research because of their flexibility and improved performance compared to standard statistical methods in developing predictive models.

Objective

This manuscript proposes an ML-enforced treatment effect estimation procedure based on an ensemble SuperLearner (SL) approach, trained on historical observational data, to control the confounding effect.

Methods

The REnal SCarring Urinary infEction trial served as a motivating example. Historical observational study data have been simulated through 10,000 Monte Carlo (MC) runs. Hypothetical RCTs have been also simulated, for each MC run, assuming different treatment effects of antibiotics combined with steroids. For each MC simulation, the SL tool has been applied to the simulated observational data. Furthermore, the average treatment effect (ATE), has been estimated on the trial data and adjusted for the SL predicted probability of renal scar.

Results

The simulation results revealed an increased power in ATE estimation for the SL-enforced estimation compared to the unadjusted estimates for all the algorithms composing the ensemble SL.

Unleashing the Power of Bayesian Re-Analysis: Enhancing Insights into Lecanemab (Clarity AD) Phase III Trial Through Informed t-Test

BACKGROUND

Clinical trials targeting Alzheimer's disease (AD) aim to alleviate clinical symptoms and alter the course of this complex neurodegenerative disorder. However, the conventional approach of null hypothesis significance testing (NHST) commonly employed in such trials has inherent limitations in assessing clinical significance and capturing nuanced evidence of effectiveness on a continuous scale.

OBJECTIVE

In this study, we conducted a re-analysis of the phase III trial of lecanemab, a recently proposed humanized IgG1 monoclonal antibody with high affinity for Aβ soluble protofibrils, using a Bayesian approach with informed t-test priors.

METHODS

To achieve this, we carefully selected trial data and derived effect size estimates for the primary endpoint, the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB). Subsequently, a series of Bayes Factor analyses were performed to compare evidence supporting the null hypothesis (no treatment effect) versus the alternative hypothesis (presence of an effect). Drawing on relevant literature and the lecanemab phase III trial, we incorporated different minimal clinically important difference (MCID) values for the primary endpoint CDR-SB as prior information.

RESULTS

Our findings, based on a standard prior, revealed anecdotal evidence favoring the null hypothesis. Additional robustness checks yielded consistent results. However, when employing informed priors, we observed varying evidence across different MCID values, ultimately indicating no support for the effectiveness of lecanemab over placebo.

CONCLUSION

Our study underscores the value of Bayesian analysis in clinical trials while emphasizing the importance of incorporating MCID and effect size granularity to accurately assess treatment efficacy.